Systems and methods for determining whether regional oximetry sensors are properly positioned

ABSTRACT

Methods and systems are presented for determining whether a regional oximetry sensor is properly positioned on a subject. First and second metric values may be determined based on respective first and second light signals. The first and second metric values and a relationship between the first and second metrics are used to determine whether the sensor is properly positioned on the subject. The first and second metrics may form a pair of metrics, and whether the sensor is properly positioned on the subject may be determined based on whether the pair of metrics falls within a sensor-on region. In some embodiments, a plurality of metrics may be determined based on a plurality of received physiological signals. The plurality of metrics may be combined, using, for example, a neural network, to determine whether the regional oximetry sensor is properly positioned on a subject.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/605,891, filed Jan. 26, 2015, now allowed, which claims the benefitof U.S. Provisional Application No. 61/932,045, filed Jan. 27, 2014,each of which are hereby incorporated by reference herein in theirentirety.

SUMMARY

The present disclosure relates to determining whether a sensor isproperly positioned on a subject, and more particularly, relates todetermining whether a regional oximetry sensor is properly positioned ona subject.

Methods and systems are provided for determining whether regionaloximetry sensors are properly positioned on tissue of a subject.

In some embodiments, a system for determining whether a regionaloximetry sensor is properly positioned on a subject includes one or moreinputs configured for receiving a first signal representative of anintensity of light at a first detector of the regional oximetry sensor,and receiving a second signal representative of an intensity of light ata second detector of the regional oximetry sensor. The system furtherincludes one or more processors configured for determining a firstmetric value based on the first signal, and determining a second metricvalue based on the second signal. The one or more processors are furtherconfigured for determining, based on the first metric value, the secondmetric value, and a relationship between the first metric and the secondmetric, whether the regional oximetry sensor is properly positioned onthe subject.

In some embodiments, a method for determining whether a regionaloximetry sensor is properly positioned on a subject comprises receivinga first signal representative of an intensity of light at a firstdetector of the regional oximetry sensor, and receiving a second signalrepresentative of an intensity of light at a second detector of theregional oximetry sensor. The method also includes determining a firstmetric value based on the first signal, and determining a second metricvalue based on the second signal. The method also includes determining,based on the first metric value, the second metric value, and arelationship between the first metric and the second metric, whether theregional oximetry sensor is properly positioned on the subject.

BRIEF DESCRIPTION OF THE FIGURES

The above and other features of the present disclosure, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings in which:

FIG. 1 is a block diagram of an illustrative physiological monitoringsystem in accordance with some embodiments of the present disclosure;

FIG. 2A shows an illustrative plot of a light drive signal in accordancewith some embodiments of the present disclosure;

FIG. 2B shows an illustrative plot of a detector signal that may begenerated by a sensor in accordance with some embodiments of the presentdisclosure;

FIG. 3 is a perspective view of an illustrative physiological monitoringsystem in accordance with some embodiments of the present disclosure;

FIG. 4 is a cross-sectional view of an illustrative regional oximetersensor unit applied to a subject's cranium in accordance with someembodiments of the present disclosure;

FIG. 5 shows an illustrative flow diagram including steps fordetermining whether a sensor is properly positioned on a subject inaccordance with some embodiments of the present disclosure;

FIG. 6 shows an illustrative plot of a sensor-on region in accordancewith some embodiments of the present disclosure;

FIG. 7 shows an illustrative plot of a sensor-on region in accordancewith some embodiments of the present disclosure;

FIG. 8 shows an illustrative plot of a sensor-on region in accordancewith some embodiments of the present disclosure;

FIG. 9 shows an illustrative flow diagram including steps fordetermining whether a sensor is properly positioned on a subject inaccordance with some embodiments of the present disclosure; and

FIG. 10 shows an illustrative block diagram for determining whether aregional oximetry sensor is properly positioned on a subject using aneural network in accordance with some embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF THE FIGURES

The present disclosure is directed towards determining whether a sensoris properly positioned on a subject. In some embodiments, the system ofthe present disclosure may be a regional oximetry system. First andsecond signals (e.g., PPG signals) may be received, the first signalrepresentative of an intensity of light received at a first detector,and the second signal representative of an intensity of light receivedat a second detector. Metric values may be determined based on the firstsignal and on the second signal (e.g., a signal level of the firstsignal and a signal level of the second signal). A determination may bemade as to whether the regional oximetry sensor is properly positionedon the subject based on the first metric value, the second metric value,and a relationship between the metrics.

In some embodiments, a first metric value and a second metric value areconsidered as pairs of metrics, and each pair of metrics is compared toa sensor-on region to determine whether the pair of metric falls withinthe sensor-on region. The sensor-on region may be based on arelationship between the first and second metrics. Pairs of metrics thatfall within the sensor-on region may indicate that the regional oximetrysensor is properly positioned on the subject. In some embodiments, twoor more metrics may be determined based on a plurality of receivedphysiological signals. The two ore more metrics may be combined, using,for example, a neural network, to determine whether the regionaloximetry sensor is properly positioned on a subject.

The foregoing techniques may be implemented in an oximeter. An oximeteris a medical device that may determine the oxygen saturation of ananalyzed tissue. One common type of oximeter is a regional oximeter. Aregional oximeter is used to estimate the blood oxygen saturation in aregion of a subject's tissue. The regional oximeter may determinedifferences in the intensity of light received for each of two or morewavelengths of light received at two different locations on thesubject's body (e.g., differential absorption values) to estimate theregional blood oxygen saturation of hemoglobin in a region of thesubject's tissue. In some embodiments, the regional oximeter may, foreach wavelength of light, compare the amount of light absorbed by thesubject's tissue in a first region to the amount of light absorbed bythe subject's tissue in a second region to derive differentialabsorption values. As opposed to pulse oximetry, which typicallyexamines the oxygen saturation of pulsatile, arterial blood, regionaloximetry examines the oxygen saturation of blood in a region of tissuethat may include blood in the venous, arterial, and capillary system.For example, a regional oximeter may include a sensor unit configuredfor placement on a subject's forehead and may be used to estimate theblood oxygen saturation of a region of tissue beneath the sensor unit(e.g., cerebral tissue).

In some embodiments, the oximeter may be a combined oximeter including aregional oximeter and a pulse oximeter. A pulse oximeter is a device fornon-invasively measuring the oxygen saturation of a patient's blood (asopposed to measuring oxygen saturation directly by analyzing a bloodsample taken from the patient). Regional, pulse, and combined oximetersmay be included in patient monitoring systems that measure and displayvarious blood flow characteristics including, but not limited to, theregional oxygen saturation of a region of tissue and the oxygensaturation of hemoglobin in arterial blood. Such patient monitoringsystems may also measure and display additional physiologicalparameters, such as a patient's pulse rate, respiration rate,respiration effort, blood pressure, any other suitable physiologicalparameter, or any combination thereof. Regional and pulse oximetry maybe implemented using a photoplethysmograph. Pulse oximeters and otherphotoplethysmograph devices may also be used to determine otherphysiological parameters and information as disclosed in: J. Allen,“Photoplethysmography And its Application in Clinical PhysiologicalMeasurement,” Physiol. Meas., vol. 28, pp. R1-R39, March 2007; W. B.Murray and P. A. Foster, “The Peripheral Pulse Wave: InformationOverlooked,” J. Clin. Monit., vol. 12, pp. 365-377, September 1996; andK. H. Shelley, “Photoplethysmography: Beyond the Calculation of ArterialOxygen Saturation and Heart Rate,” Anesth. Analg., vol. 105, pp.S31-S36, December 2007; all of which are incorporated by referenceherein in their entireties.

An oximeter may include a light sensor that is placed at a site on apatient, typically a fingertip, toe, forehead or earlobe, or in the caseof a neonate, across a foot or hand. The oximeter may use a light sourceto pass light through blood perfused tissue and photoelectrically sensethe absorption of the light in the tissue. Additional suitable sensorlocations include, without limitation, the neck to monitor carotidartery pulsatile flow, the wrist to monitor radial artery pulsatileflow, the inside of a patient's thigh to monitor femoral arterypulsatile flow, the ankle to monitor tibial artery pulsatile flow,around or in front of the ear, locations with strong pulsatile arterialflow, and locations above tissue desired to be monitored. Suitablesensors for these locations may include sensors that detect reflectedlight.

The oximeter may measure the intensity of light that is received at thelight sensor as a function of time. The oximeter may also includesensors at multiple locations. A signal representing light intensityversus time or a mathematical manipulation of the signal (e.g., a scaledversion thereof, a log taken thereof, a scaled version of log takenthereof, an inverted signal, etc.) may be referred to as thephotoplethysmograph (PPG) signal. In addition, the term “PPG signal,” asused herein, may also refer to an absorption signal (i.e., representingthe amount of light absorbed by the tissue) or any suitable mathematicalmanipulation thereof. The light intensity or the amount of lightabsorbed may then be used to calculate any of a number of physiologicalparameters, including an amount of a blood constituent (e.g.,oxyhemoglobin) being measured as well as a pulse rate and when eachindividual pulse occurs.

In some embodiments, the photonic signal interacting with the tissue isof one or more wavelengths that are attenuated by the blood in an amountrepresentative of the blood constituent concentration. In someembodiments, red and infrared (IR) wavelengths may be used because ithas been observed that highly oxygenated blood will absorb relativelyless red light and more IR light than blood with a lower oxygensaturation. In some embodiments, different infrared (IR) wavelengths maybe used. By comparing the intensities of two wavelength at differentpoints in the pulse cycle, it is possible to estimate the blood oxygensaturation of hemoglobin in arterial blood.

The system may process data to determine physiological parameters usingtechniques well known in the art. For example, the system may determinearterial blood oxygen saturation using two wavelengths of light and aratio-of-ratios calculation. In another example, the system maydetermine regional blood oxygen saturation using multiple wavelengths oflight and a differential absorption technique. The system also mayidentify pulses and determine pulse amplitude, respiration, bloodpressure, other suitable parameters, or any combination thereof, usingany suitable calculation techniques. In some embodiments, the system mayuse information from external sources (e.g., tabulated data, secondarysensor devices) to determine physiological parameters.

In some embodiments, the regional oximeter may include a first sensorlocated at a first distance from the light source (e.g., the neardetector) and a second sensor located at a second farther distance fromthe light source (e.g., the far detector). In some embodiments, theregional oximeter may include a near detector at a distance of 3centimeters (cm) and a far detector at a distance of 4 cm from the lightsource, which may include, for example, one or more emitters. Thedistance between each detector and the light source affects the meanpath length of the detected light and thus the depth of tissue throughwhich the respective received wavelength of light passes. In otherwords, the light detected by the near detector may pass through shallow,superficial tissue, whereas the light detected by the far detector maypass through additional, deep tissue. In some embodiments, the regionaloximeter's light source may include two or more emitters and one or moredetectors. For example, a first emitter may be located a short distancefrom a detector, and the second emitter may be located a longer distancefrom the detector.

In some embodiments, multiple wavelengths of light may be received atboth the near and far detectors, and the absorption of the multiplewavelengths of light may be computed and contrasted at each detector toderive regional blood oxygen saturation. For example, light signals forfour wavelengths of light may be received at each of the near and fardetectors, and the amount of light of each wavelength received at thenear detector may be subtracted from the amount of light of eachwavelength received at the far detector. In some embodiments, the amountof absorption computed at the near detector for each wavelength may besubtracted from the corresponding amount of the absorption computed atthe far detector. The resulting light signals or absorptions may be usedto compute the regional blood oxygen saturation of a region of deeptissue through which light received at the far detector passed. Becausethe far detector receives light that passed through deep tissue inaddition to the shallow tissue through which the light passes and isreceived at the near detector, the regional saturation may be computedfor just the deep tissue by subtracting out the amount of light receivedby the near detector or the corresponding absorption. For example, aregional oximeter on a subject's forehead may include near and fardetectors spaced from the light source such that the near detectorreceives light that passes through the subject's forehead tissue,including the superficial skin, shallow tissue covering the skull, andthe skull, and the far detector receives light that passes through theforehead tissue and brain tissue (i.e., cerebral tissue). In theexample, the differences in the amounts of light received by the nearand far detectors may be used to derive an estimate of the regionalblood oxygen saturation of the subject's cerebral tissue (i.e., cerebralblood oxygen saturation).

The following description and accompanying FIGS. 1-10 provide additionaldetails and features of some embodiments of the present disclosure.

FIG. 1 is a block diagram of an illustrative physiological monitoringsystem 100 in accordance with some embodiments of the presentdisclosure. System 100 may include a sensor 102 and a monitor 104 forgenerating and processing physiological signals of a subject. In someembodiments, sensor 102 and monitor 104 may be part of an oximeter.

Sensor 102 of physiological monitoring system 100 may include lightsource 130, detector 140, and detector 142. Light source 130 may beconfigured to emit photonic signals having two or more wavelengths oflight (e.g., red and IR) into a subject's tissue. For example, lightsource 130 may include a red light emitting light source and an IR lightemitting light source, (e.g., red and IR light emitting diodes (LEDs)),for emitting light into the tissue of a subject to generatephysiological signals. In one embodiment, the red wavelength may bebetween about 600 nm and about 700 nm, and the IR wavelength may bebetween about 800 nm and about 1000 nm. It will be understood that lightsource 130 may include any number of light sources with any suitablecharacteristics. In embodiments where an array of sensors is used inplace of single sensor 102, each sensor may be configured to emit asingle wavelength. For example, a first sensor may emit only a red lightwhile a second may emit only an IR light. In some embodiments, lightsource 130 may be configured to emit two or more wavelengths ofnear-infrared light (e.g., wavelengths between 600 nm and 1000 nm) intoa subject's tissue. In some embodiments, light source 130 may beconfigured to emit four wavelengths of light (e.g., 724 nm, 770 nm, 810nm, and 850 nm) into a subject's tissue.

It will be understood that, as used herein, the term “light” may referto energy produced by radiative sources and may include one or more ofultrasound, radio, microwave, millimeter wave, infrared, visible,ultraviolet, gamma ray or X-ray electromagnetic radiation. As usedherein, light may also include any wavelength within the radio,microwave, infrared, visible, ultraviolet, or X-ray spectra, and thatany suitable wavelength of electromagnetic radiation may be appropriatefor use with the present techniques. Detectors 140 and 142 may be chosento be specifically sensitive to the chosen targeted energy spectrum oflight source 130.

In some embodiments, detectors 140 and 142 may be configured to detectthe intensity of multiple wavelengths of near-infrared light. In someembodiments, detectors 140 and 142 may be configured to detect theintensity of light at the red and IR wavelengths. In some embodiments,an array of sensors may be used and each sensor in the array may beconfigured to detect an intensity of a single wavelength. In operation,light may enter detector 140 after passing through the subject's tissue,including skin, bone, and other shallow tissue (e.g., non-cerebraltissue and shallow cerebral tissue). Light may enter detector 142 afterpassing through the subject's tissue, including skin, bone, othershallow tissue (e.g., non-cerebral tissue and shallow cerebral tissue),and deep tissue (e.g., deep cerebral tissue). Detectors 140 and 142 mayconvert the intensity of the received light into an electrical signal.The light intensity may be directly related to the absorbance and/orreflectance of light in the tissue. That is, when more light at acertain wavelength is absorbed or reflected, less light of thatwavelength is received from the tissue by detectors 140 and 142. Afterconverting the received light to an electrical signal, detectors 140 and142 may send the detection signals to monitor 104, where the detectionsignals may be processed and physiological parameters may be determined(e.g., based on the absorption of the red and IR wavelengths in thesubject's tissue at both detectors). In some embodiments, one or more ofthe detection signals may be preprocessed by sensor 102 before beingtransmitted to monitor 104. In some embodiments, sensor 102 may includeadditional sensors elements, such as additional light detectors or othertypes of sensor elements such as impedance detectors.

In the embodiment shown, monitor 104 includes control circuitry 110,light drive circuitry 120, front end processing circuitry 150, back endprocessing circuitry 170, user interface 180, and communicationinterface 190. Monitor 104 may be communicatively coupled to sensor 102using, for example, one or more inputs.

Control circuitry 110 may be coupled to light drive circuitry 120, frontend processing circuitry 150, and back end processing circuitry 170, andmay be configured to control the operation of these components. In someembodiments, control circuitry 110 may be configured to provide timingcontrol signals to coordinate their operation. For example, light drivecircuitry 120 may generate one or more light drive signals, which may beused to turn on and off the light source 130, based on the timingcontrol signals. The front end processing circuitry 150 may use thetiming control signals to operate synchronously with light drivecircuitry 120. For example, front end processing circuitry 150 maysynchronize the operation of an analog-to-digital converter and ademultiplexer with the light drive signal based on the timing controlsignals. In addition, the back end processing circuitry 170 may use thetiming control signals to coordinate its operation with front endprocessing circuitry 150.

Light drive circuitry 120, as discussed above, may be configured togenerate a light drive signal that is provided to light source 130 ofsensor 102. The light drive signal may, for example, control theintensity of light source 130 and the timing of when light source 130 isturned on and off. In some embodiments, light drive circuitry 120 maycomprise a power supply and a switch for selectively applying power tolight source 130. When light source 130 is configured to emit two ormore wavelengths of light, the light drive signal may be configured tocontrol the operation of each wavelength of light. The light drivesignal may comprise a single signal or may comprise multiple signals(e.g., one signal for each wavelength of light). In some embodiments,light drive circuitry 130 provides one or more light drive signals tolight source 130.

FIG. 2A shows an illustrative plot of a light drive signal including redlight drive pulse 202 and IR light drive pulse 204 in accordance withsome embodiments of the present disclosure. In the illustratedembodiment, light drive pulses 202 and 204 are shown as square waves. Itwill be understood that square waves are presented merely as anillustrative example, not by way of limitation, and that these pulsesmay include any other suitable signal, for example, shaped pulsewaveforms, rather than a square waves. The shape of the pulses may begenerated by a digital signal generator, digital filters, analogfilters, any other suitable equipment, or any combination thereof. Forexample, light drive pulses 202 and 204 may be generated by light drivecircuitry 120 under the control of control circuitry 110. As usedherein, drive pulses may refer to the high and low states of a pulse,switching power or other components on and off, high and low outputstates, high and low values within a continuous modulation, othersuitable relatively distinct states, or any combination thereof. Thelight drive signal may be provided to light source 130, including redlight drive pulse 202 and IR light drive pulse 204 to drive red and IRlight emitters, respectively, within light source 130.

Red light drive pulse 202 may have a higher amplitude than IR lightdrive 204 since red LEDs may be less efficient than IR LEDs atconverting electrical energy into light energy. In some embodiments, theoutput levels may be equal, may be adjusted for nonlinearity ofemitters, may be modulated in any other suitable technique, or anycombination thereof. Additionally, red light may be absorbed andscattered more than IR light when passing through perfused tissue.

When the red and IR light sources are driven in this manner they emitpulses of light at their respective wavelengths into the tissue of asubject in order generate physiological signals that physiologicalmonitoring system 100 may process to calculate physiological parameters.It will be understood that the light drive amplitudes of FIG. 2A aremerely exemplary and that any suitable amplitudes or combination ofamplitudes may be used, and may be based on the light sources, thesubject tissue, the determined physiological parameter, modulationtechniques, power sources, any other suitable criteria, or anycombination thereof. It will also be understood that in systems that usemore than two wavelengths of light, additional light drive pulses may beincluded in the light drive signal. For example, when four wavelengthsof light are used, four light drive pulses, one for each wavelength oflight, may be included in the light drive signal.

The light drive signal of FIG. 2A may also include “off” periods 220between the red and IR light drive pulse. “Off” periods 220 are periodsduring which no drive current may be applied to light source 130. “Off”periods 220 may be provided, for example, to prevent overlap of theemitted light, since light source 130 may require time to turncompletely on and completely off. The period from time 216 to time 218may be referred to as a drive cycle, which includes four segments: a redlight drive pulse 202, followed by an “off” period 220, followed by anIR light drive pulse 204, and followed by an “off” period 220. Aftertime 218, the drive cycle may be repeated (e.g., as long as a lightdrive signal is provided to light source 130). It will be understoodthat the starting point of the drive cycle is merely illustrative andthat the drive cycle can start at any location within FIG. 2A, providedthe cycle spans two drive pulses and two “off” periods. Thus, each redlight drive pulse 202 and each IR light drive pulse 204 may beunderstood to be surrounded by two “off” periods 220. “Off” periods mayalso be referred to as dark periods, in that the emitters are dark orreturning to dark during that period. It will be understood that theparticular square pulses illustrated in FIG. 2A are merely exemplary andthat any suitable light drive scheme is possible. For example, lightdrive schemes may include shaped pulses, sinusoidal modulations, timedivision multiplexing other than as shown, frequency divisionmultiplexing, phase division multiplexing, any other suitable lightdrive scheme, or any combination thereof.

Referring back to FIG. 1, front end processing circuitry 150 may receivedetection signals from detectors 140 and 142 and provide two or moreprocessed signals to back end processing circuitry 170. In someembodiments, front end processing circuitry 150 may receive thedetection signals from one or more inputs of monitor 104. The term“detection signals,” as used herein, may refer to any of the signalsgenerated within front end processing circuitry 150 as it processes theoutput signal of detectors 140 and 142. Front end processing circuitry150 may perform various analog and digital processing of the detectorsignals. One suitable detector signal that may be received by front endprocessing circuitry 150 is shown in FIG. 2B.

FIG. 2B shows an illustrative plot of detector current waveform 214 thatmay be generated by a sensor in accordance with some embodiments of thepresent disclosure. The peaks of detector current waveform 214 mayrepresent current signals provided by a detector, such as detectors 140and 142 of FIG. 1, when light is being emitted from a light source. Theamplitude of detector current waveform 214 may be proportional to thelight incident upon the detector. The peaks of detector current waveform214 may be synchronous with drive pulses driving one or more emitters ofa light source, such as light source 130 of FIG. 1. For example,detector current peak 226 may be generated in response to a light sourcebeing driven by red light drive pulse 202 of FIG. 2A, and peak 230 maybe generated in response to a light source being driven by IR lightdrive pulse 204. Valley 228 of detector current waveform 214 may besynchronous with periods of time during which no light is being emittedby the light source, or the light source is returning to dark, such as“off” period 220. While no light is being emitted by a light sourceduring the valleys, detector current waveform 214 may not fall all theway to zero.

It will be understood that detector current waveform 214 may be an atleast partially idealized representation of a detector signal, assumingperfect light signal generation, transmission, and detection. It will beunderstood that an actual detector current will include amplitudefluctuations, frequency deviations, droop, overshoot, undershoot, risetime deviations, fall time deviations, other deviations from the ideal,or any combination thereof. It will be understood that the system mayshape the drive pulses shown in FIG. 2A in order to make the detectorcurrent as similar as possible to idealized detector current waveform214.

Referring back to FIG. 1, front end processing circuitry 150, which mayreceive detection signals, such as detector current waveform 214, mayinclude analog conditioning 152, analog-to-digital converter (ADC) 154,demultiplexer 156, digital conditioning 158, decimator/interpolator 160,and ambient subtractor 162.

Analog conditioning 152 may perform any suitable analog conditioning ofthe detector signals. The conditioning performed may include any type offiltering (e.g., low pass, high pass, band pass, notch, or any othersuitable filtering), amplifying, performing an operation on the receivedsignal (e.g., taking a derivative, averaging), performing any othersuitable signal conditioning (e.g., taking a derivative, averaging),performing any other suitable signal conditioning (e.g., converting acurrent signal to a voltage signal), or any combination thereof.

The conditioned analog signals may be processed by analog-to-digitalconverter 154, which may convert the conditioned analog signals intodigital signals. Analog-to-digital converter 154 may operate under thecontrol of control circuitry 110. Analog-to-digital converter 154 mayuse timing control signals from control circuitry 110 to determine whento sample the analog signal. Analog-to-digital converter 154 may be anysuitable type of analog-to-digital converter of sufficient resolution toenable a physiological monitor to accurately determine physiologicalparameters. In some embodiments, analog-to-digital converter 154 may bea two channel analog-to-digital converter, where each channel is usedfor a respective detector waveform.

Demultiplexer 156 may operate on the analog or digital form of thedetector signals to separate out different components of the signals.For example, detector current waveform 214 of FIG. 2B includes a redcomponent corresponding to peak 226, an IR component corresponding topeak 230, and at least one ambient component corresponding to valley228. Demultiplexer 156 may operate on detector current waveform 214 ofFIG. 2B to generate a red signal, an IR signal, a first ambient signal(e.g., corresponding to the ambient component corresponding to valley228 that occurs immediately after the peak 226), and a second ambientsignal (e.g., corresponding to the ambient component corresponding tovalley 228 that occurs immediately after the IR component 230).Demultiplexer 156 may operate under the control of control circuitry110. For example, demultiplexer 156 may use timing control signals fromcontrol circuitry 110 to identify and separate out the differentcomponents of the detector signals.

Digital conditioning 158 may perform any suitable digital conditioningof the detector signals. Digital conditioning 158 may include any typeof digital filtering of the signal (e.g., low pass, high pass, bandpass, notch, averaging, or any other suitable filtering), amplifying,performing an operation on the signal, performing any other suitabledigital conditioning, or any combination thereof.

Decimator/interpolator 160 may decrease the number of samples in thedigital detector signals. For example, decimator/interpolator 160 maydecrease the number of samples by removing samples from the detectorsignals or replacing samples with a smaller number of samples. Thedecimation or interpolation operation may include or be followed byfiltering to smooth the output signal.

Ambient subtractor 162 may operate on the digital signal. In someembodiments, ambient subtractor 162 may remove dark or ambientcontributions to the received signal.

The components of front end processing circuitry 150 are merelyillustrative and any suitable components and combinations of componentsmay be used to perform the front end processing operations.

The front end processing circuitry 150 may be configured to takeadvantage of the full dynamic range of analog-to-digital converter 154.This may be achieved by applying gain to the detection signals by analogconditioning 152 to map the expected range of the detection signals tothe full or close to full output range of analog-to-digital converter154. The output value of analog-to-digital converter 154, as a functionof the total analog gain applied to each of the detection signals, maybe given as:ADC Value=Total Analog Gain×[Ambient Light+LED Light]

Ideally, when ambient light is zero and when the light source is off,the analog-to-digital converter 154 will read just above the minimuminput value. When the light source is on, the total analog gain may beset such that the output of analog-to-digital converter 154 may readclose to the full scale of analog-to-digital converter 154 withoutsaturating. This may allow the full dynamic range of analog-to-digitalconverter 154 to be used for representing the detection signals, therebyincreasing the resolution of the converted signal. In some embodiments,the total analog gain may be reduced by a small amount so that smallchanges in the light levels incident on the detectors do not causesaturation of analog-to-digital converter 154.

However, if the contribution of ambient light is large relative to thecontribution of light from a light source, the total analog gain appliedto the detection current may need to be reduced to avoid saturatinganalog-to-digital converter 154. When the analog gain is reduced, theportion of the signal corresponding to the light source may map to asmaller number of analog-to-digital conversion bits. Thus, more ambientlight noise in the input of analog-to-digital converter 154 may resultin fewer bits of resolution for the portion of the signal from the lightsource. This may have a detrimental effect on the signal-to-noise rationof the detection signals. Accordingly, passive or active filtering orsignal modification techniques may be employed to reduce the effect ofambient light on the detection signals that applied to analog-to-digitalconverter 154, and thereby reduce the contribution of the noisecomponent to the converted digital signal.

Back end processing circuitry 170 may include processor 172 and memory174. Processor 172 may be adapted to execute software which may includean operating system and one or more applications, as part of performingthe functions described herein. Processor 172 may receive and furtherprocess physiological signals received from front end processingcircuitry 150. For example, processor 172 may determine one or morephysiological parameters based on the received physiological signals.Processor 172 may include an assembly of analog or digital electroniccomponents. Processor 172 may calculate physiological information. Forexample, processor 172 may compute one or more of regional oxygensaturation, blood oxygen saturation (e.g., arterial, venous, or both),pulse rate, respiration rate, respiration effort, blood pressure,hemoglobin concentration (e.g., oxygenated, deoxygenated, and/or total),any other suitable physiological parameters, or any combination thereof.In another example, processor 172 may compute metric values based onreceived physiological signals. Processor 172 may perform any suitablesignal processing of a signal, such as any suitable band-pass filtering,adaptive filtering, closed-loop filtering, any other suitable filtering,and/or any combination thereof. Processor 172 may also receive inputsignals from additional sources not shown. For example, processor 172may receive an input signal containing information about treatmentsprovided to the subject from user interface 180. Additional inputsignals may be used by processor 172 in any of the calculations oroperations it performs in accordance with back end processing circuitry170 or monitor 104.

Memory 174 may include any suitable computer-readable media capable ofstoring information that can be interpreted by processor 172. In someembodiments, memory 174 may store relationship information, historicaldata, sensor information, sensor-on regions, metrics, metric values,calibration information, predetermined thresholds, calculated values,such as regional blood oxygen saturation, blood oxygen saturation, pulserate, blood pressure, fiducial point locations or characteristics,initialization parameters, any other calculated values, or anycombination thereof, in a memory device for later retrieval. Thisinformation may be data or may take the form of computer-executableinstructions, such as software applications, that cause themicroprocessor to perform certain functions and/or computer-implementedmethods. Depending on the embodiment, such computer-readable media mayinclude computer storage media and communication media. Computer storagemedia may include volatile and non-volatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, program modulesor other data. Computer storage media may include, but is not limitedto, RAM, ROM EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, DVD, or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by components of the system. Back endprocessing circuitry 170 may be communicatively coupled with userinterface 180 and communication interface 190.

User interface 180 may include user input 182, display 184, and speaker186. User interface 180 may include, for example, any suitable devicesuch as one or more medical devices (e.g., a medical monitor thatdisplays various physiological parameters, a medical alarm, or any othersuitable medical device that either displays physiological parameters oruses the output of back end processing 170 as an input), one or moredisplay devices (e.g., monitor, personal digital assistant (PDA), mobilephone, tablet computer, any other suitable display device, or anycombination thereof), one or more audio devices, one or more memorydevices (e.g., hard disk drive, flash memory, RAM, optical disk, anyother suitable memory device, or any combination thereof), one or moreprinting devices, any other suitable output device, or any combinationthereof.

User input 182 may include any type of user input device such as akeyboard, a mouse, a touch screen, buttons, switches, a microphone, ajoy stick, a touch pad, or any other suitable input device. The inputsreceived by user input 182 can include information about the subject,such as age, weight, height, diagnosis, medications, treatments, and soforth. The inputs received by user input 182 may also includeinformation about the sensor, for example, the type or model of sensor,an indication of whether the sensor is an infant sensor or an adultsensor, an indication of where, on the subject, the sensor is configuredfor positioning, any other sensor specifications, or any combinationthereof.

In an embodiment, the subject may be a medical patient and display 184may exhibit a list of values which may generally apply to the subject,such as, for example, age ranges or medication families, which the usermay select using user input 182. Additionally, display 184 may display,for example, a subject's regional oxygen saturation generated by monitor104 (referred to as an “rSO₂” measurement), a subject's blood oxygensaturation generated by monitor 104 (referred to as an “SpO₂”measurement), an estimate of a subject's venous oxygen saturationgenerated by monitor 104 (referred to as an “S_(Y)O_(Z)” measurement),sensor information, calibration information, metrics, sensor-on orsensor-off indications, pulse rate information, respiration rateinformation, blood pressure, any other parameters, and any combinationthereof. Display 184 may include any type of display such as a cathoderay tube display, a flat panel display such a liquid crystal display(LCD), LED display, or plasma display, or any other suitable displaydevice. Speaker 186 within user interface 180 may provide an audiblesound that may be used in various embodiments, such as for example,sounding an audible alarm in the event that a patient's physiologicalparameters are not within a predefined normal range or in the event thatthe sensor is not properly positioned on the patient.

Communication interface 190 may enable monitor 104 to exchangeinformation with external devices. Communications interface 190 mayinclude any suitable hardware, software, or both, which may allowmonitor 104 to communicate with electronic circuitry, a device, anetwork, a server or other workstations, a display, or any combinationthereof. Communications interface 190 may include one or more receivers,transmitters, transceivers, antennas, plug-in connectors, ports, inputs,communications buses, communications protocols, device identificationprotocols, any other suitable hardware or software, or any combinationthereof. Communications interface 190 may be configured to allow wiredcommunication (e.g., using USB, RS-232, Ethernet, or other standards),wireless communication (e.g., using WiFi, IR, WiMax, BLUETOOTH, USB, orother standards), or both. For example, communications interface 190 maybe configured using a universal serial bus (USB) protocol (e.g., USB2.0, USB 3.0), and may be configured to couple to other devices (e.g.,remote memory devices storing templates), using a four-pin USB standardType-A connector (e.g., plug and/or socket) and cable. In someembodiments, communications interface 190 may include an internal bussuch as, for example, one or more slots for insertion of expansioncards.

It will be understood that the components of physiological monitoringsystem 100 that are shown and described as separate components are shownand described as such for illustrative purposes only. In someembodiments the functionality of some of the components may be combinedin a single component. For example, the functionality of front endprocessing circuitry 150 and back end processing circuitry 170 may becombined in a single processor system. Additionally, in some embodimentsthe functionality of some of the components of monitor 104 shown anddescribed herein may be divided over multiple components. For example,some or all of the functionality of control circuitry 110 may beperformed in front end processing circuitry 150, in back end processingcircuitry 170, or both. In addition, while a single processor isdepicted in FIG. 1, it will be understood that one or more processorsmay be used to perform the functionality described above. In otherembodiments, the functionality of one or more of the components may beperformed in a different order or may not be required. In an embodiment,all of the components of physiological monitoring system 100 can berealized in processor circuitry.

FIG. 3 is a perspective view of an illustrative physiological monitoringsystem 310 in accordance with some embodiments of the presentdisclosure. In some embodiments, one or more components of physiologicalmonitoring system 310 may include one or more components ofphysiological monitoring system 100 of FIG. 1. Physiological monitoringsystem 310 may include sensor 312 and monitor 314. In some embodiments,sensor unit 312 may be part of an oximeter. Sensor unit 312 may includeone or more light source 316 for emitting light at one or morewavelengths into a subject's tissue. Detectors 318 and 338 may also beprovided in sensor unit 312 for detecting the light that is reflected byor has traveled through the subject's tissue. Any suitable configurationof light source 316 and detectors 318 and 338 may be used. In someembodiments, sensor unit 312 may include multiple light sources anddetectors, which may be spaced apart. In some embodiments, detector 318(i.e., the near detector) may be positioned at a location closer tolight source 316 than detector 338 (i.e., the far detector).Physiological monitoring system 310 may also include one or moreadditional sensor units (not shown) that may, for example, take the formof any of the embodiments described herein with reference to sensor unit312. An additional sensor unit may be the same type of sensor unit assensor unit 312, or different sensor unit type than sensor unit 312(e.g., a photoacoustic sensor). Multiple sensor units may be capable ofbeing positioned at two different locations on a subject's body.

In some embodiments, sensor unit 312 may be connected to monitor 314 asshown. Sensor unit 312 may be powered by an internal power source, e.g.,a battery (not shown). Sensor unit 312 may draw power from monitor 314.In another embodiment, the sensor may be wirelessly connected (notshown) to monitor 314. Monitor 314 may be configured to calculatephysiological parameters based at least in part on data relating tolight emission and acoustic detection received from one or more sensorunits such as sensor unit 312. For example, monitor 314 may beconfigured to determine regional oxygen saturation, pulse rate,respiration rate, respiration effort, blood pressure, blood oxygensaturation (e.g., arterial, venous, or both), hemoglobin concentration(e.g., oxygenated, deoxygenated, and/or total), any other suitablephysiological parameters, or any combination thereof. In someembodiments, calculations may be performed on the sensor units or anintermediate device and the result of the calculations may be passed tomonitor 314. In some embodiments, monitor 314 may be configured todetermine metric values based on received physiological signals (e.g.,from sensor unit 312) and to determine whether a sensor is properlypositioned on a subject based on the metric values and relationshipsbetween the metrics. Monitor 314 may also be configured to determinewhether pairs of metric values fall within sensor-on regions. Further,monitor 314 may include display 320 configured to display thephysiological parameters or other information about the system (e.g., anindication that the sensor is or is not properly positioned on asubject's tissue). In the embodiment shown, monitor 314 may also includea speaker 322 to provide an audible sound that may be used in variousother embodiments, such as for example, sounding an audible alarm in theevent that metric values do not fall within a sensor-on region. In someembodiments, physiological monitoring system 310 may include astand-alone monitor in communication with the monitor 314 via a cable ora wireless network link. In some embodiments, monitor 314 may beimplemented as monitor 104 of FIG. 1.

In some embodiments, sensor unit 312 may be communicatively coupled tomonitor 314 via cable 324 at port 336. Cable 324 may include electronicconductors (e.g., wires for transmitting electronic signals fromdetectors 318 and 338), optical fibers (e.g., multi-mode or single-modefibers for transmitting emitted light from light source 316), any othersuitable components, any suitable insulation or sheathing, or anycombination thereof. In some embodiments, a wireless transmission device(not shown) or the like may be used instead of or in addition to cable324. Monitor 314 may include a sensor interface configured to receivephysiological signals from sensor unit 312, provide signals and power tosensor unit 312, or otherwise communicate with sensor unit 312. Thesensor interface may include any suitable hardware, software, or both,which may allow communication between monitor 314 and sensor unit 312.

In some embodiments, physiological monitoring system 310 may includecalibration device 380. Calibration device 380, which may be powered bymonitor 314, a battery, or by a conventional power source such as a walloutlet, may include any suitable calibration device. Calibration device380 may be communicatively coupled to monitor 314 via communicativecoupling 382, and/or may communicate wirelessly (not shown). In someembodiments, calibration device 380 is completely integrated withinmonitor 314. In some embodiments, calibration device 380 may include amanual input device (not shown) used by an operator to manually inputreference signal measurements obtained from some other source (e.g., anexternal invasive or non-invasive physiological measurement system).

In the illustrated embodiment, physiological monitoring system 310includes a multi-parameter physiological monitor 326. The monitor 326may include a cathode ray tube display, a flat panel display (as shown)such as an LCD display, an LED display, or a plasma display, or mayinclude any other type of monitor now known or later developed.Multi-parameter physiological monitor 326 may be configured to calculatephysiological parameters and to provide a display 328 for informationfrom monitor 314 and from other medical monitoring devices or systems(not shown). For example, multi-parameter physiological monitor 326 maybe configured to display an indication as to whether a regional oximetrysensor unit (e.g., sensor unit 312) is properly positioned on a subjectand the subject's regional blood oxygen saturation generated by monitor314. In another example, multi-parameter physiological monitor 326 maybe configured to display sensor information received from monitor 314 orfrom a sensor unit (not shown). Multi-parameter physiological monitor326 may include a speaker 330.

Monitor 314 may be communicatively coupled to multi-parameterphysiological monitor 326 via cable 332 or 334 that is coupled to asensor input port or a digital communications port, respectively and/ormay communicate wirelessly (not shown). In addition, monitor 314 and/ormulti-parameter physiological monitor 326 may be coupled to a network toenable the sharing of information with servers or other workstations(not shown). Monitor 314 may be powered by a battery (not shown) or by aconventional power source such as a wall outlet.

In some embodiments, any of the processing components and/or circuits,or portions thereof, of FIGS. 1 and 3, including sensors 102 and 312 andmonitors 104, 314, and 326 may be referred to collectively as processingequipment. For example, processing equipment may be configured toamplify, filter, sample and digitize input signals from sensor 102 or312 (e.g., using an analog-to-digital converter), determine metrics fromthe input signals, and determine whether a sensor is properly positionedbased on the metrics. The processing equipment may include one or moreprocessors. In some embodiments, all or some of the components of theprocessing equipment may be referred to as a processing module orprocessing circuitry. In some embodiments, the processing equipment maybe part of a regional oximetry system, and sensors 102 and 312 of FIGS.1 and 3 may correspond to regional oximeter sensor unit 400 of FIG. 4,described below.

FIG. 4 is a cross-sectional view of an illustrative regional oximetersensor unit 400 applied to a subject's cranium in accordance with someembodiments of the present disclosure. Regional oximeter sensor unit 400includes light source 402, near detector 404, and far detector 406 andis shown as positioned on a subject's forehead 412. In the illustratedembodiment, light source 402 generated a light signal, which is showntraveling first and second mean path lengths 408 and 410 to respectivenear and far detectors 404 and 406. As shown, first and second mean pathlengths 408 and 410 traverse the subject's cranial structure atdifferent depths. The subject's cranial structure includes outer skin414, shallow tissue 416, and cranial bone 416 (i.e., the frontal shellof the skull). Beneath cranial bone 416 is Dura Mater 420 and cerebraltissue 422.

In some embodiments, light source 402 of sensor unit 400 may include oneor more emitters for emitting light into the tissue of a subject togenerate physiological signals. Detectors 404 and 406 may be positionedon sensor unit 400 such that near detector 404 is located at distance d₁from light source 402 and far detector 406 is located at a distance d₂from light source 402. As shown, distance d₁ is shorter than distanced₂, and it will be understood that any suitable distances d₁ and d₂ maybe used such that mean path length 408 of light detected by neardetector 404 is shorter than the mean path length 410 of far detector406. Near detector 404 may receive the light signal after it hastraveled first mean path length 408, and far detector 406 may receivethe light signal after it has traveled second mean path length 410.First mean path length 408 may traverse the subject's outer skin 414,shallow tissue 416, cranial bone 416, and Dura Mater 420. In someembodiments, first mean path length 408 may also traverse shallowcerebral tissue 422. Second mean path length 410 may traverse thesubject's outer skin 414, shallow tissue 416, cranial bone 416, DuraMater 420, and cerebral tissue 422.

In some embodiments, regional oximeter sensor unit 400 may be part of aregional oximetry system for determining the amount of light absorbed bya region of a subject's tissue. as described in detail above, lightsource 402 may emit multiple wavelengths of light (e.g., in the RED, theIR, or the RED and IR range of wavelengths of light) and for eachwavelength of light, an absorption value may be determined based on theamount of light received at near detector 404, and an absorption valuemay be determined based on the amount of light received at far detector406. For each wavelength of light, a differential absorption value maybe computed based on the difference between the absorption valuesdetermined for near detector 404 and far detector 406. The differentialabsorption values may be representative of the amount of light absorbedby cerebral tissue 422 at each wavelength. Using known methods, theprocessing equipment may determine an rSO₂ value for a region of thesubject's tissue based on the differential absorption values. In theillustrated embodiment, an rSO₂ value may be determined for a region ofthe subject's cerebral tissue 422. It will be understood that while theforegoing techniques for determining rSO₂ were described with referenceto cerebral tissue, rSO₂ may be calculated for any suitable region of asubject's tissue. In some embodiments, the detected light signals may benormalized, for example, based on the amount of light emitted by lightsource 402, characteristics of the detectors, system gains, othersuitable properties of the system, and/or empirical data prior todetermining the absorption values.

In practice, a regional oximetry system may be used to monitor asubject's rSO₂ while the subject is undergoing a surgical procedure(e.g., cardiac surgery). During a cardiac surgical procedure, the heartmay be stopped, and the laminar arterial blood flow may be maintained bya cardiac bypass machine. Thus, a PPG signal, generated by a regionaloximetry system during this procedure, would reflect only the DC signalcomponent (i.e., tissue, venous, and laminar/constant arterial flow) andnot the pulsatile AC signal component (i.e., because the heart is notpumping blood. Hence, in this application, the regional oximetry systemdetermines a subject's rSO₂ based only on the DC signal component. Thiscan make it difficult to determine whether a sensor is properlypositioned on a subject using conventional sensor-off techniques. Forexample, conventional sensor-off techniques cannot reliably distinguishbetween sensor application on human tissue or on an inanimate object. Inaccordance with the present disclosure, processing equipment maydetermine metrics, based on light intensity signals received atdetectors of a regional oximetry sensor, and the metrics may provideinformation differentiating between sensor applications on tissue or offtissue. In some embodiments, the processing equipment may determinewhether a sensor is properly positioned on a subject based on first andsecond metrics values and the relationship between the first and secondmetrics, as shown in FIG. 5.

FIG. 5 shows an illustrative flow diagram 500 including steps fordetermining whether a sensor is properly positioned on a subject inaccordance with some embodiments of the present disclosure. The steps offlowchart 500 may be implemented as part of a regional oximetry system.The processing equipment may generate a light drive signal configured tocause one or more light sources to emit light corresponding to two ormore wavelengths of light. The one or more light sources may correspondto light source 130 of FIG. 1, 316 of FIG. 3, or 402 of FIG. 4. In someembodiments, the one or more light sources may be part of a regionaloximetry sensor. The light drive signal may correspond to the lightdrive signal shown in FIG. 2A.

At step 502, the processing equipment may receive first and second lightsignals. In some embodiments, the first signal is representative of anintensity of light received at a first detector of a regional oximetrysensor and the second light signal is representative of an intensity oflight received at a second detector of the regional oximetry sensor. Insome embodiments, the first and second light signals may each berepresentative of two or more wavelengths of light. It will beunderstood that light signals may be received at more than two detectorsof a regional oximetry sensor. It will also be understood that the lightsignals may correspond to any suitable number of wavelengths of light.In some embodiments, the first and second signals may correspond to PPGsignals. In some embodiments, the first signal may correspond to lightthat traveled a first mean path length to a first detector, and thesecond signal may correspond to light that traveled a second mean pathlength to a second detector. For example, the first and second signalsmay correspond to light that traveled respective mean path lengths 408and 410 to respective near and far detectors 404 and 406 of FIG. 4. asdescribed above, the first signal may correspond to light attenuated bya first region of tissue, and the second signal may correspond to lightattenuated by a second region of tissue. In some embodiments, the firstregion may correspond to a smaller, shallow region of tissue than thesecond region, which may correspond to a larger, deep region of tissue.For example, the processing equipment may be implemented as part of acerebral oximeter, where the first region may include the subject'souter skin, shallow tissue, cranial bone, Dura Mater, and shallowcerebral tissue and the second region may include all of the componentsof the first region and the subject's deeper cerebral tissue.

At step 504, the processing equipment may determine first and secondmetric values based on the received light signals. In some embodiments,the first metric value is determined based on the first light signal andthe second metric value is determined based on the second signal. Insome embodiments, the processing equipment may determine first andsecond metric values based on processed first and second signals. Forexample, the processing equipment may normalize the first and secondlight signals before determining metric values. In some embodiments, theprocessing equipment may normalize the first and second signals based onrespective first and second calibration coefficients. The signals may benormalized using any suitable processing equipment including, forexample, processor 172 of FIG. 1, or monitor 314 or multi-parameterphysiological monitor 326 of FIG. 3. In some embodiments, the processingequipment may determine the calibration coefficients based on at leastone of the brightness of the light source, the sensitivity of therespective detector, system gains, other suitable properties of theprocessing equipment, and/or empirical data. In some embodiments, theprocessing equipment may determine a first calibration coefficient basedon the brightness of the light source (e.g., light source 402 of FIG. 4)and the sensitivity of the first detector (e.g., near detector 404 ofFIG. 4). In some embodiments, the processing equipment may determine asecond calibration coefficient based on the brightness of the lightsource and the sensitivity of the second detector (e.g., far detector406 of FIG. 4). It will be understood that more than one light sourcemay be used, and the processing equipment may determine the calibrationcoefficients based on the brightness of the respective light source. Insome embodiments, the processing equipment may receive calibrationinformation from the sensor (e.g., regional oximeter sensor unit 400 ofFIG. 4). Calibration information, as used herein, may include thesensitivity of each of the sensor's detectors, the brightnesscharacteristics of each of the sensor's light sources, the wavelengthsof light the sensor's light sources are configured to emit, theconfiguration of the light sources and detectors, any other suitablesensor characteristics, or any combination thereof. In some embodiments,the processing equipment may determine the first and second calibrationcoefficients based on the calibration information. For example, theprocessing equipment may received calibration information indicating thesensitivities of the first and second detectors and determine the firstand second calibration coefficients based on the sensitivity of therespective detector.

In some embodiments, the first and second metrics may be associated withsignal levels of the respective first and second signals. In someembodiments, the processing equipment may compute a first metric valueas the logarithm of the first normalized signal and a second metricvalue as the logarithm of the second normalized signal. For example, thefirst metric value may correspond to the natural logarithm of an 810 nmwavelength component of the first normalized signal detected at a firstdetector positioned 30 mm from the light source, where the first signalis normalize to correct for variations in light source (e.g., LED) anddetector efficiency. In another example, the second metric value maycorrespond to the natural logarithm of an 810 nm wavelength component ofthe second normalized signal detected at a second detector positioned 40mm from the light source, where the second signal is normalized tocorrect for variations in light source (e.g., LED) and detectorefficiency. In some embodiments, the processing equipment may detectvariations in the levels of the first and second signals. In someembodiments, the processing equipment may determine first and secondmetric values based on the detected variations in the levels of therespective first and second signals. It will be understood that theprocessing equipment may determine any number of suitable metrics basedon any number of suitable signals. For example, the processing equipmentmay determine a third metric value based on the first signal and afourth metric value based on the second signal. In another example, theprocessing equipment may determine third and fourth metric valuesassociated with one or more wavelength components of the first andsecond signals that are different from the one or more wavelengthcomponents used to determine the first and second metric values. It willbe understood that each wavelength component of a signal may also bereferred to herein as a signal.

In some embodiments, the processing equipment may determine first andsecond morphology metric values associated respectively with themorphology of the first and second signals. Morphology metrics, as usedherein, may include suitable signal values, signal morphologies, outputvalues from suitable operations performed on the signal or othermetrics, any other suitable mathematical characterizations, or anysuitable combinations thereof. For example, morphology metrics mayinclude pulse wave area (PWA), geometric centroid of a pulse wave, rateof change computed at one or more points of a time series (e.g.,derivative of any suitable order of a signal), statistics of a signal(e.g., mean, moment of any suitable order, regression parameters),offset of a signal from a baseline, interval of portion of a signal(e.g., length of upstroke), relative position of a fiducial point of asignal (e.g., dichrotic notch position), any other suitable metric orchange thereof, or any suitable combinations thereof. In someembodiments, the processing equipment may determine a first morphologymetric value based on the skewness (e.g., the standardized third centralmoment) of the first derivative of the first signal. In someembodiments, the processing equipment may determine a second morphologymetric value based on the peak-to-peak modulation of the second signal.It will be understood that the processing equipment may determine anynumber of suitable morphology metrics associated with the morphologiesof any number of suitable signals. For example, the processing equipmentmay determine a third morphology metric value associated with themorphology of the first signal and a fourth morphology metric valueassociated with the morphology of the second signal. In another example,the processing equipment may determine third and fourth morphologymetric values associated respectively with the morphologies of third andfourth signals.

At step 506, the processing equipment may determine, based on the firstand second metric values and a relationship between the first metric andthe second metric, whether the regional oximetry sensor is properlypositioned on the subject. In some embodiments, the relationship betweenthe first and second metrics may be based on possible pairs of first andsecond metric values. In some embodiments, a relationship between firstand second metrics may include any suitable mapping, pairing,association, or other suitable relationship between values of the firstand second metrics such that it may be determined whether the sensor isproperly positioned on the subject. For example, the relationshipbetween first and second metrics may define values of the first metricand corresponding values of the second metric that fall within or definea sensor-on region. In some embodiments, the relationship between thefirst and second metrics may define a sensor-on region based on thepossible pairs of first and second metric values. In some embodiments,the processing equipment may consider the first and second metric valuesas a pair of metrics and determine whether the pair of metrics fallswithin a sensor-on region, where the sensor-on region is determinedbased on a relationship between the first and second metrics. In someembodiments, the sensor-on region may be represented as a region in anx-y plane, where the x-axis is associated with a metric and the y-axisis associated with a metric. For example, the x-axis may correspond tovalues of the first metric, the y-axis may correspond to values of thesecond metric, and the sensor-on region may correspond to an area on theplot defined by a relationship between the first metric and the secondmetric. It will be understood that the representation of thesensor-region as a region in the x-y plane is merely illustrative andthat a sensor-on region may correspond to a collection of data points,an array of metric values, data in a lookup table, ranges of metricvalues, any other suitable collection of values of metrics, or anycombination thereof. In some embodiments, one metric may be usedtogether with the relationship information to determine one or morethresholds or a range of values against which another metric isevaluated. For example, the first metric value may be used to determinethe upper of and lower bounds of the sensor-on region for the secondmetric. The second metric may then be evaluated to determine whether itis between the upper and lower bounds to determine whether the sensor isproperly positioned on the subject. In some embodiments, the processingequipment may determine a sensor-on region based on relationshipinformation. In some embodiments, the processing equipment may receiverelationship information from memory (e.g., memory 174 of FIG. 1), thesensor (e.g., regional oximeter sensor unit 400 of FIG. 4), or from acentralized information system (e.g., a hospital information system, anyother suitable external source, or any combination thereof. As usedherein, relationship information includes data indicative of anysuitable relationship between metrics. In some embodiments, theprocessing equipment may receive relationship information based onhistorical data. In some embodiments, the processing equipment maydetermine a relationship between the first and second metrics based onhistorical data. Historical data may include, for example, historicalcalibration studies. In some embodiments, the historical data mayinclude a plurality of first and second metric values recorded forsensors properly positioned to a plurality of subjects, and arelationship between the first and second metrics may be indicative ofpairs of the first and second metric values associated with a sensorproperly positioned to a subject. In some embodiments, the processingequipment may determine a sensor-on region based on a distribution ofthe historical data. For example, the historical data points may beplotted in the x-y plane, where the x-axis corresponds to values of thefirst metric, the y-axis corresponds to values of the second metric, andthe processing equipment may determine the sensor-on region based on thedistribution of the historical data points. In some embodiments, theprocessing equipment may receive relationship information that defines asensor-on region, which may be represented in an x-y plane, and theprocessing equipment may determine whether pairs of first and secondmetric values fall within the sensor-on region.

FIGS. 6-8 show illustrative plots of sensor-on regions in accordancewith some embodiments of the present disclosure. It will be understoodthat the particular plots shown, and the metrics of those plots, aremerely exemplary. For purposes of brevity and clarity, and not by way oflimitation, FIGS. 6-8 depict sensor-on regions as two-dimensional areain the x-y plane. It will be understood that, sensor-on regions are notlimited to these depictions and may correspond to any suitablecollections of data points, relationships, and number of metrics.

FIG. 6 shows an illustrative plot 600 of sensor-on region 606 inaccordance with some embodiments of the present disclosure. Thehorizontal axis of plot 600 corresponds to first metric 602, and thevertical axis corresponds to second metric 604.

In some embodiments, the processing equipment may determine whether asensor is properly positioned on a subject's tissue based on a firstmetric value, a second metric value, and a relationship between thefirst and second metrics (e.g., a sensor-on region). In someembodiments, the processing equipment may determine a first metric basedon a first normalized signal and a second metric based on a secondnormalized signal. In some embodiments, the first and second signals maycorrespond to the first and second light signals described in step 502of FIG. 5. For example, the first signal may correspond to an 810 nmwavelength component of light detected at a first detector positioned 30mm from the light source, and the second signal may correspond to an 810nm wavelength component of light detected at a second detectorpositioned 40 mm from the light source. As described above in referenceto step 504 of FIG. 5, the processing equipment may normalize the firstand second signals based on respective first and second calibrationcoefficients. In the embodiment shown, first metric 602 corresponds tothe signal level of the first normalized signal, and second metric 604corresponds to the signal level of the second normalized signal. Firstmetric values x_(i) are given by

$\begin{matrix}{{\ln\left( \frac{{First}\mspace{14mu}{Signal}}{{First}\mspace{14mu}{Calibration}\mspace{14mu}{Coefficient}} \right)},} & (1)\end{matrix}$and second metric values y_(i) are given by

$\begin{matrix}{{\ln\left( \frac{{Second}\mspace{14mu}{Signal}}{{Second}\mspace{14mu}{Calibration}\mspace{14mu}{Coefficient}} \right)},} & (2)\end{matrix}$i ∈

⁺,

⁺ denotes the set of positive integers, and the first and secondcalibration coefficients may be based on the brightness of the lightsource and the sensitivity of the respective first and second detectors,as described above with reference to step 504 of FIG. 5. In someembodiments, the sensor-on region 606, shown by a dashed line, may bebased on a relationship between first metric 602 and second metric 604,such that pairs of metrics (x_(i), y_(i)) that fall within sensor-onregion 606 may be indicative of a sensor properly positioned on asubject's tissue. For example, the processing equipment may determine afirst metric value x₁ based on eq. 1 and a second metric value y₁ basedon eq. 2. The processing equipment may consider the first and secondmetric values as a pair of metrics (x₁, y₁), and determine if the pairof metrics (x₁,y₁) falls within sensor-on region 606. In the example,pair of metrics (x₁, y₁) may correspond to point 608, which theprocessing equipment may determine is within sensor-on region 606. Insome embodiments, the processing equipment may determine that a sensoris properly positioned on a subject's tissue if a pair of first andsecond metric values is associated with a point inside the sensor-onregion. In another example, the processing equipment may determine afirst metric value x₂ on eq. 1 and a second metric value y₂ based on eq.2, and the corresponding pair of metrics (x₂, y₂) may correspond topoint 612. In some embodiments, the processing equipment may determinethat a sensor is not properly positioned on a subject's tissue if a pairof first and second metric values is associated with a point outside thesensor-on region. The processing equipment may determine that point 612falls outside of sensor-on region 606. In some embodiments, theprocessing equipment may determine a pair of metric values correspondingto point 610, which is shown as falling on the edge of sensor-on region606. In some embodiments, the processing equipment may determine thatpoint 610 is within the sensor-on region, and in some embodiments, theprocessing equipment may determine that point 610 is outside of thesensor-on region. In some embodiments, the processing equipment maydiscard point 610 and determine a new pair of values for the firstmetric 602 and second metric 604. In some embodiments, first metric 602and second metric 64 may correspond to variations in the levels of therespective first and second signals. In some embodiments, the processingequipment may determine whether a sensor is properly positioned on asubject's tissue based on combinations of metrics, as shown in FIG. 7.

FIG. 7 shows an illustrative plot 700 of sensor-on region 706 inaccordance with some embodiments of the present disclosure. Thehorizontal axis and the vertical axis of plot 700 correspond torespective combination metrics 702 and 704. Sensor-on region 706 isdepicted with two levels circumscribed by concentric ellipses 708 and710.

In some embodiments, the processing equipment may determine whether asensor is properly positioned on a subject's tissue based on non-linearcombinations of metrics. In the embodiment shown, the processingequipment may determine combination metric 702 based on a combination offirst metric 602 and second metric 604 of FIG. 6. In some embodiments,values of combination metric 702 are given byFirst Metric−linreg(Second Metric),  (3)and values of metric 704 are give bySecond Metric+0.05*(Second Metric)²  (4)where “first metric” denotes first metric 602 of FIG. 6, “second metric”denotes second metric 604 of FIG. 6, and linreg denotes the linearregression operation. In some embodiments, eqs. 3 and 4 make take asinput values of first metric 602 computed based on eq. 1, and values ofsecond metric 604 computed based on eq. 2. In some embodiments,combination metric 702 make be indicative of the combined distributionof first metric 602 and second metric 604. In some embodiments, theprocessing equipment may determine values of combination metric 702using eq. 3, which takes as input the linear regression of second metric604 given by:linreg(Second Metric)=m*Second Metric+b  (5)where m and b denote linear regress coefficients, which may beempirically defined. In some embodiments, metric 704 may be indicativeof the distribution of second metric 604. In some embodiments, theprocessing equipment may determine values of metric 704 using eq. 4,which includes a quadratic term (0.05*(Second Metric)²) to compensatefor the skewed distribution of second metric 604. In some embodiments,the processing equipment may determine whether a sensor is properlypositioned on a subject's tissue based on combination metric 702,combination metric 704, and sensor-on region 706. In some embodiments,the processing equipment may determine combination metric 702 based on anear-infrared light component of the first signal and combination metric704 based on a near-infrared light component of the second signal.

In some embodiments, the processing equipment may determine sensor-onregion 706 based on the distribution of historical data, as describedabove in reference to step 506 of FIG. 5. In some embodiments, sensor-onregion 706 may be partitioned into two levels, shown as ellipse 708 andellipse 710. In some embodiments, ellipse 708 may correspond to twostandard deviations from the center of the historical data distribution,and ellipse 710 may correspond to three standard deviations from thecenter of the historical data distribution. In some embodiments, theprocessing equipment may determine a value of combination metric 702 anda value of combination metric 704 as a pair of metrics associated withboth of the first and second signals, as described above in connectionwith step of FIG. 5. The processing equipment may determine if the pairof metrics falls within sensor-on region 706. For example, the pair ofmetrics may correspond to point 712 of plot 700, which the processingequipment may determine is within sensor-on region 706, and morespecifically, within ellipse 708 (i.e., within two standard deviations).In another example, the pair of metrics may correspond to point 714 ofplot 700, which the processing equipment may determine is withinsensor-on region 706, and more specifically, within ellipse 710 (i.e.,within three standard deviations). In some embodiments, the processingequipment may determine that a sensor is properly positioned on asubject's tissue if a pair of metric values is associated with a pointinside the sensor-on region. In another example, the pair of metrics maycorrespond to point 716 of plot 700, which the processing equipment maydetermine is not with sensor-on region 706. In some embodiments, theprocessing equipment may determine that a sensor is not properlypositioned on a subject's tissue if a pair of first and second metricvalues is associated with a point outside the sensor-on region.

In some embodiments, the processing equipment may compare values ofcombination metrics 702 and 704 to a distribution threshold to determinewhether a sensor is properly positioned on a subject's tissue. In someembodiments, the distribution threshold is a function of combinationmetrics 702 and 704, and the processing equipment may compare a value ofcombination metric 702 and a value of combination metric 704 to thedistribution threshold. In some embodiments, the distribution thresholdmay be based on a function that circumscribes a two-dimensionalsensor-on region, for example, sensor-on region 706 of plot 700. In someembodiments, the processing equipment may compare a value of combinationmetric 702 to a first distribution threshold and a value of combinationmetric 704 to a second distribution threshold and determine whether ornot the pair of metrics falls within sensor-on region 706 based on thecomparisons. For example, if the value of combination metric 702 exceedsthe first distribution threshold, and the value of combination metric704 does not exceed the second distribution threshold, then theprocessing equipment may determine that the pair of metrics does notfall within sensor-on region 706. In some embodiments, the processingequipment may determine more than one level of distribution threshold.For example, a first level distribution threshold may correspond toellipse 708 and a second level distribution threshold may correspond toellipse 710. The processing equipment may select and apply one of theplurality of thresholds based on the values of additional metrics orsensor characteristics, or may use the plurality of thresholds tocontrol the display of user messages (e.g., deciding whether to display“sensor off” or “adjust sensor”) and the timing thereof.

FIG. 8 shows an illustrative plot 800 of sensor-on region 806 inaccordance with some embodiments of the present disclosure. Thehorizontal axis of plot 800 corresponds to first morphology metric 802,and the vertical axis corresponds to second morphology metric 804.Sensor-on region 806 is depicted with two levels circumscribed byconcentric ellipses 808 and 810.

In some embodiments, the processing equipment may determine whether asensor is properly positioned on a subject's tissue based on whetherpairs of values of morphology metrics fall within a sensor-on region. Inthe embodiment shown, the processing equipment may determine a value offirst morphology metric 802 and a value of second morphology metric 804.In some embodiments, the processing equipment may determine firstmorphology metric 802 based on a shape of the pulsatile modulation in afirst signal. For example, a first morphology metric 802 may correspondto the shape of the pulsatile modulation in an 810 nm wavelengthcomponent of light detected at a first detector (e.g., detector 404 ofFIG. 4) positioned 30 nm from the light source (e.g., light source 402of FIG. 4). The processing equipment may use first morphology metric 802to distinguish between the typical shape of a signal attenuated by atissue site with pulsatile arterial blood as opposed to the shape of asignal attenuated by other non-tissue mediums. In some embodiments, theprocessing equipment may determine values of first morphology metric 802based on the skewness (e.g., the standardized third central moment) of aderivative of the first signal. In some embodiments, the processingequipment may determine values of first morphology metric 802 byfiltering the first signal using a highpass filter with a cornerfrequency of approximately 0.25 Hz, compressing the first signal'samplitude over a dynamic range based on the percent pulsatile modulationof the first signal to minimize the impact of large transient artifacts,and calculating the skewness of the first derivative of the compressedwaveform. In some embodiments, the processing equipment does notsubtract an ambient light estimate from the first signal beforecomputing values of first morphology metric 802, because ambient lightmay be modulated by the pulsatile blood flow, and because the ambientlight signal may add noise to the morphology calculation. It will beunderstood that while first morphology metric 802 has been described asdetermined based on a light signal received at the near detector, firstmorphology metric 802 may be determined based on any suitable signal,including the signal received at the far detector. The signal detectedat the near detector has the advantage of being stronger than the signaldetected at the far detector, so the processing equipment may determinea more accurate assessment of pulse shape based on the near signal. Insome embodiments, the processing equipment may determine secondmorphology metric 804 based on a percentage modulation of a secondsignal. For example, second morphology metric 804 may correspond to thenatural logarithm of the percentage of peak-to-peak pulsatile modulationof an 810 nm wavelength component of light detected at a detector (e.g.,detector 406 of FIG. 4) positioned 40 mm from the light source (e.g.,light source 402 of FIG. 4). In some embodiments, the processingequipment may compute the peak-to-peak modulation of the second signalas:

$\begin{matrix}{\frac{{Amplitude}_{\max} - {Amplitude}_{\min}}{{Average}\mspace{14mu}{Amplitude}},} & (6)\end{matrix}$where the amplitude values are computed over a 1 second window. Theprocessing equipment may then average the output of eq. 6 over a 4second window. The processing equipment may use second morphology metric804, computed using eq. 6, to distinguish pulse amplitudes that aretypical of a signal attenuated by a tissue site with pulsing arterialblood as opposed to a “flat line” (i.e., no pulsatile modulation) orlarge transient amplitudes associated with sensor manipulation (e.g.,artifact).

In some embodiments, the processing equipment may determine sensor-onregion 806 based on the distribution of historical data, as describedabove in reference to step 506 of FIG. 5. In some embodiments, sensor-onregion 806 may be partitioned into two levels, shown as ellipse 808 andellipse 810. In some embodiments, ellipse 808 may correspond to twostandard deviations from the center of the historical data distribution,and ellipse 810 may correspond to three standard deviations from thecenter of the historical data distribution. In some embodiments, theprocessing equipment may determine a value of first morphology metric802 and a value of second morphology metric 804 and consider the valuesas a pair of metrics associated with the morphologies of respectivefirst and second signals, as described above in connection with step 506of FIG. 5. The processing equipment may determine if the pair of metricsfalls within sensor-on region 806. For example, the pair of metrics maycorrespond to point 812 of plot 800, which the processing equipment maydetermine is with sensor-on region 806, and more specifically, withinellipse 808 (i.e., within two standard deviations). In another example,the pair of metrics may correspond to point 814 of plot 800, which theprocessing equipment may determine is within sensor-on region 806, andmore specifically, within ellipse 810 (i.e., within three standarddeviations). In some embodiments, the processing equipment may determinethat a sensor is properly positioned on a subject's tissue if a pair ofmetric values is associated with a point inside the sensor-on region. Inanother example, the pair of metrics may correspond to point 816 of plot800, which the processing equipment may determine is not withinsensor-on region 806. In some embodiments, the processing equipment maydetermine that a sensor is not properly positioned on a subject's tissueif a pair of first and second morphology metric values is associatedwith a point outside the sensor-on region.

It will be understood that the particular plots shown in FIGS. 6-8 aremerely exemplary and are presented as non-limiting illustrations. Forexample, it will be understood that any suitable combination ofindividual metrics, combination metrics, and morphology metrics may beused to determine whether a sensor is properly positioned.

FIG. 9 shows an illustrative flow diagram 900 including steps fordetermining whether a sensor is properly positioned on a subject inaccordance with some embodiments of the present disclosure. The steps offlowchart 900 may be implemented as part of a regional oximetry system.The processing equipment may include one or more light sources foremitting a plurality of wavelengths of light. The one o more lightsources may correspond to light source 130 of FIG. 1, 316 of FIG. 3, or402 of FIG. 4. In some embodiments, the one or more light sources may bepart of a regional oximetry sensor.

At step 902, the processing equipment may receive a plurality ofphysiological signals. In some embodiments, step 902 may correspond tostep 502 of FIG. 5. As described above, the processing equipment mayreceive a plurality of physiological signals at first and seconddetectors of a regional oximetry sensor. In some embodiments, thephysiological signals may be representative of an intensity of lightreceived at a respective detector. In some embodiments, thephysiological signals may correspond to PPG signals.

At step 904, the processing equipment may receive sensor information. Insome embodiments, the processing equipment may receive sensorinformation from the monitor memory (e.g., memory 174 of FIG. 1), thesensor (e.g., regional oximeter sensor unit 400 of FIG. 4, or from acentralized information system (e.g., a hospital information system),any other suitable external source, or any combination thereof. Sensorinformation, as used herein, may include the type or mode of sensor, anindication of whether the sensor is an infant sensor or an adult sensor,an indication of where, on the subject, the sensor is configured forpositioning, any other sensor specifications, or any combinationthereof. For example, the processing equipment may receive sensorinformation indicating that the sensor is an infant sensor configuredfor positioning on an infant's forehead (i.e., a cerebral oximeter).

At step 906, the processing equipment may determine a plurality ofmetrics based on the plurality of physiological signals. In someembodiments, step 906 may correspond to step 504 of FIG. 5. As describedabove, in some embodiments, the processing equipment may determine firstand second metrics based respectively on first and second physiologicalsignals. In some embodiments, the processing equipment may determine aplurality of metrics based on the first signal and a plurality ofmetrics based on the second signal. In some embodiments, the processingequipment may receive three physiological signals (e.g., representativeof intensities of light received at three detectors) and determine afirst metric based on the first signal, a second metric based on thesecond signal, and a third metric based on the third signal. In someembodiments, the processing equipment may determine a plurality ofmetrics for each of a plurality of physiological signals. For example,the processing equipment may determine a plurality of metrics for eachof first, second, and third physiological signals.

In some embodiments, as described above in reference to step 504 of FIG.5, the processing equipment may determine first and second morphologymetric values associated respectively with the morphologies of first andsecond physiological signals. For example, the processing equipment maydetermine a first morphology metric value based on the shape of thepulsatile modulation of the first signal (e.g., shallow signal travelingmean path length 408 to near detector 404 of FIG. 4). In anotherexample, the first morphology metric value may be determined based onthe skewness of a first derivative of the first signal. In anotherexample, the processing equipment may determine a second morphologymetric value based on the peak-to-peak modulation of the second signal(e.g., deep signal traveling mean path length 410 to far detector 406 ofFIG. 6). In some embodiments, the processing equipment may determine aplurality of morphology metrics associated with a plurality ofphysiological signals.

In some embodiments, the processing equipment may determine any numberof additional metrics based on the received physiological signals.Additional metrics may include, for example, ambient light levels, pulse(AC) amplitudes and shapes, large transient variations in signal levelscorrelations between pulsatile parts of any two emitter/detector pairs,the history of any metrics during a monitoring session, the history ofthe sensor-off assessment (e.g., hysteresis), metrics based onnon-optical signals (e.g., a metric based on an impedance signal), anyother suitable metrics, or any combination thereof. In some embodiments,the processing equipment may determine a metric value based on anambient light component of a received physiological signal. For example,the processing equipment may determine an ambient light metric valuebased on the natural logarithm of the ambient light component of a lightsignal received at a detector positioned 40 mm from a light source on aregional pulse oximetry sensor (e.g., regional oximetry sensor unit 400of FIG. 4). In some embodiments, a second ambient light metric value maybe derived from the ambient light received at a detector position 30 mmfrom the light source. In some embodiments, the processing equipment maydetermine the ambient light metric value when the light source is off.In some embodiments, the processing equipment may determine a firsttrend parameter based on the values of a first metric over time and asecond trend parameter based on the values of a second metric over time.For example, the processing equipment may determine first and secondtrend parameters based respectively on the values of first and secondmetrics over time, where the first and second metrics are basedrespectively on the signal levels of first and second normalized signals(e.g., first metric 602 and second metric 604 of FIG. 6). In someembodiments, the processing equipment may detect variations in thelevels of first and second signals and determine respective first andsecond metric values based on the detected signal level variations. Itwill be understood that while physiological signals have been discussedas corresponding to light signals, this is merely illustrative, notlimiting, and the processing equipment may determine metric values basedon any suitable physiological signals. In some embodiments, theprocessing equipment may receive an impedance signal and determine ametric value based on the impedance signal.

At step 908, the processing equipment may determine, based on theplurality of metrics and/or the sensor information, whether a sensor isproperly positioned on a subject. In some embodiments, step 908 maycorrespond to step 506 of FIG. 5. In some embodiments, the processingequipment may combine the plurality of metric values and the sensorinformation to determine whether the sensor is properly positioned onthe subject. In some embodiments, the plurality of metrics maycorrespond to the plurality of metric values determined in step 906, andthe sensor information may correspond to the sensor information receivedin step 904. For example, the processing equipment may determine whetherthe sensor is properly positioned on the subject based on first andsecond metric values based respectively on the signal levels of firstand second normalized signals, a third metric value based on animpedance signal, an ambient light metric based on the ambient lightcomponent of the second signal, first and second morphology metricsbased respectively on the morphologies of the first and second signals,and the sensor information. The sensor information may include, forexample, a sensor type identifying whether the regional oximetry sensoris an infant sensor or an adult sensor, an indication of how well thesensor shields the detectors from ambient light, and/or sensorinformation indicative of where, on the subject, the regional oximetrysensor is configured for positioning. In some embodiments, theprocessing equipment may determine whether pairs of the plurality ofmetrics fall within respective sensor-on regions, where the sensor-onregions are associated with a relationship between the paired metrics.For example, the processing equipment may determine whether a pair ofvalues of signal level metrics (e.g., metrics 602 and 604 of FIG. 6)falls within a sensor-on region (e.g., sensor-on region 606 of FIG. 6).In some embodiments, the processing equipment may assign a score to thepair of metrics based on whether or not it falls within the sensor-onregion (e.g., a score of “0” for “yes” and “1” for “no”). In anotherexample, the processing equipment may determine a score of “2” for apair of metrics that is determined to fall within two standarddeviations (e.g., ellipse 708 of FIG. 7, ellipse 808 of FIG. 8) of thecenter of a distribution sensor-on region and a score of “3” for a pairof metrics falling within three standard deviations (e.g., ellipse 710of FIG. 7, ellipse 810 of FIG. 8). In some embodiments, the processingequipment may assign a score to each of the plurality of metricsdetermined in step 906. For example, the processing equipment may assigna score of “0” to metrics indicating the sensor is properly positionedand a “1” to metrics indicating the sensor may not be properlypositioned. The processing equipment may combine the assigned scoresusing an average, a weighted average (e.g., pairs associated with pointspartially inside and partially outside of a sensor-on region may beweighed heavier), a summation, any other suitable technique forcombining the scores to form a combined score, or any combinationthereof. In some embodiments, the processing equipment may determinethat a sensor is not properly positioned on the subject of a tissue ifthe combined score exceeds a threshold. In some embodiments theprocessing equipment may determine that a sensor is not properlypositioned on a subject's tissue if any of the metrics is indicative ofan improper position or any of the pairs of metrics fall outside asensor region. In some embodiments, the processing equipment maydetermine that a sensor is properly positioned on a subject's tissue ifa majority of the metrics and pairs of metrics are indicative of aproperly positioned sensor. In some embodiments, the processingequipment may combine the plurality of metrics and the sensorinformation using a neural network, as shown in FIG. 10.

FIG. 10 shows an illustrative block diagram 1000 for determining whethera regional oximetry sensor is properly positioned on a subject using aneural network in accordance with some embodiments of the presentdisclosure. Neural network 1002 may be implemented using any suitableprocessing equipment including, for example, processor 172 of FIG. 1. Insome embodiments, neural network 1002 may be trained empirically, usinghistorical data or training data indicative of sensors properlypositioned on tissue and sensors positioned on non-tissue. In someembodiments, neural network 1002 may be trained using two or moredetectors, and each detector may have a different degree of sensitivityto ambient light. In some embodiments, neural network 1002 may betrained using a Levenberg-Marquardt training method. In someembodiments, neural network 1002 may be a fully inter-connected,feed-forward neural network, including eight nodes in a single hiddenlayer and a logarithmic sigmoid transfer function. In some embodiments,neural network 1002 may receive inputs that have been calculated over a64-sample window (e.g., corresponding to 4-5 seconds).

In some embodiments, neural network 1002 may input the plurality ofmetrics determined in step 906. In the embodiment shown, neural network1002 may input metric values 1004-1012 and sensor information 1014. Insome embodiments, metrics 1004 and 1006 may correspond to first andsecond metrics 602 and 604 of FIG. 6 metric 1008 may correspond toambient light metric determined in step 906 above, metrics 1012 and 1010may correspond to first and second morphology metrics 802 and 804 ofFIG. 8, and sensor information 1014 may correspond to the signalinformation received in step 904 above. In some embodiments, neuralnetwork 1002 may determine, based on input metrics 1004-1012 and/orsensor information 1014 whether a sensor is properly positioned on asubject. In some embodiments, neural network 1002 may combine inputmetrics 1004-1012 and/or sensor information 1014 to determine whetherthe sensor is properly positioned on the subject. It will be understoodthat neural network 1002 is provided as an illustrative example, not byway of limitation, and that any suitable processing module or techniquemay be used to combine the metrics, including, for example, statemachines, fuzzy logic techniques, Bayesian logic techniques, Markovmodels, evolutionary/genetic algorithms, any other suitable processingtechnique or module, or an combination thereof.

Referring back to FIG. 9, at step 910, the processing equipment maydetermine the rSO₂ of a region of the subject's tissue when it isdetermined that the sensor is properly positioned on the subject. Insome embodiments, the rSO₂ value may be displayed on display 184 of FIG.1, display 328 of multi-parameter physiological monitor 326 or display320 of monitor 314 of FIG. 3, or any other suitable display fordepicting physiological information. In some embodiments, when it isdetermined that the regional oximetry sensor is not properly positionedon the subject, the processing equipment may display a sensor offmessage. In some embodiments, the processing equipment may sound anaudible alarm using, for example, speaker 186 of FIG. 1. In someembodiments, the processing equipment may not display a sensor offmessage or an rSO₂ value. In some embodiments, the processing equipmentmay include other messages or indicators.

It will be understood that the steps above are exemplary and that insome implementations, steps may be added, removed, omitted, repeated,reordered, modified in any other suitable way, or any combinationthereof. For example, in some embodiments, step 904 may be omitted, andat step 908, the processing equipment may determine based on theplurality of metrics whether the sensor is properly positioned on asubject.

It will be understood that noise, such as motion artifact, may causeinstantaneous metric values to fall outside of a sensor-on region eventhough the sensor is properly positioned. In order to prevent falsedeterminations of sensor off, the determination of whether a sensor isproperly positioned (e.g., at step 506 of FIG. 5 and step 908 of FIG. 9)may include additional processing steps. For example, the metric valuesmay be averaged over time to prevent short-term deviations due to noisefrom causing a sensor off determination. As another example, a sensoroff determination may be declared after the metrics fall outside of asensor-on region for a predetermined continuous amount of time. Theseexamples are merely illustrative and any suitable techniques may be usedto prevent false determinations of sensor off.

The foregoing is merely illustrative of the principles of thisdisclosure, and various modifications may be made by those skilled inthe art without departing from the scope of this disclosure. Theabove-described embodiments are presented for purposes of illustrationand not by way of limitation. The present disclosure also can take manyforms other than those explicitly described herein. Accordingly, it isemphasized that this disclosure is not limited to the explicitlydisclosed methods, systems, and apparatuses, but is intended to includevariations to and modifications thereof, which are within the spirit ofthe following claims.

What is claimed is:
 1. A system comprising: one or more inputsconfigured to: receive a first signal representative of an intensity oflight at a first detector of a regional oximetry sensor, and receive asecond signal representative of an intensity of light at a seconddetector of the regional oximetry sensor; and one or more processorsconfigured to: determine a first metric value based on the first signal;determine a second metric value based on the second signal; determine afirst combination metric value based on the first metric value and thesecond metric value; determine a second combination metric value basedon the second metric value, and determine, based on the firstcombination metric value and the second combination metric value, thatthe regional oximetry sensor is properly positioned relative to a targetsite on a subject.
 2. The system of claim 1, wherein the one or moreprocessors are configured to determine that the regional oximetry sensoris properly positioned relative to the target site by at least comparingthe first combination metric value and the second combination metricvalue to a distribution threshold.
 3. The system of claim 1, wherein theone or more processors are configured to determine that the regionaloximetry sensor is properly positioned relative to the target site by atleast comparing the first combination metric value to a firstdistribution threshold and comparing the second combination metric valueto a second distribution threshold.
 4. The system of claim 3, whereinthe one or more processors are configured to determine that the regionaloximetry sensor is properly positioned relative to the target site inresponse to determining that the first combination metric value exceedsthe first distribution threshold and that the second combination metricdoes not exceed the second distribution threshold.
 5. The system ofclaim 1, wherein the one or more processors are configured to use aneural network to determine, based on the first combination metric valueand the second combination metric value, that the regional oximetrysensor is properly positioned relative to the target site.
 6. The systemof claim 1, wherein the one or more processors are further configured todetermine the regional oxygen saturation (rSO₂) of the subject inresponse to determining that the regional oximetry sensor is positionedwithin or at the target site.
 7. The system of claim 1, wherein thefirst metric value is based on a near-infrared light component of thefirst signal, and the second metric value is based on a near-infraredlight component of the second signal, and wherein the one or moreprocessors are configured to determine that the regional oximetry sensoris properly positioned relative to the target site by at least comparingthe first combination metric value and the second combination metricvalue to a distribution threshold, wherein the distribution threshold isa function of the first metric value and the second metric value.
 8. Thesystem of claim 1, wherein the one or more processors are configured todetermine the first metric value based on the first signal by at leastnormalizing the first signal based on a first calibration coefficient togenerate a normalized first signal, and determining the first metricvalue based on the normalized first signal, and wherein the one or moreprocessors are configured to determine the second metric value based onthe second signal by at least normalizing the second signal based on asecond calibration coefficient to generate a normalized second signal,and determining the second metric value based on the normalized secondsignal.
 9. The system of claim 8, wherein the regional oximetry sensorcomprises a light source configured to emit a plurality of wavelengthsof light, wherein the first and second detectors of the regionaloximetry sensor are configured to receive the plurality of wavelengthsof light, and wherein: the first calibration coefficient is based on abrightness of the light source and a first sensitivity of the firstdetector, and the second calibration coefficient is based on thebrightness of the light source and a second sensitivity of the seconddetector.
 10. The system of claim 8, wherein the one or more processorsare configured to determine at least one of the first metric value orthe second metric value by at least determining the at least one of thefirst metric value or the second metric value as a logarithm of arespective one of the normalized first signal or the normalized secondsignal.
 11. The system of claim 1, wherein the one or more processorsare configured to determine the first combination metric value by atleast determining First Metric−m*Second Metric+b, wherein m and b denotelinear regression coefficients, wherein First Metric denotes the firstmetric value, and wherein Second Metric denotes the second metric value.12. The system of claim 1, wherein the one or more processors arefurther configured to: determine a first morphology metric valueassociated with morphology of the first signal; and determine a secondmorphology metric value associated with morphology of the second signal,wherein the one or more processors are further configured to determinethat the regional oximetry sensor is properly positioned relative to thetarget site based on the first and second morphology metric values. 13.The system of claim 12, wherein the first signal is associated with afirst mean path length and the second signal is associated with a secondmean path length longer than the first mean path length, and wherein theone or more processors are configured to determine the first morphologymetric value based on a shape of a pulsatile modulation of the firstsignal.
 14. The system of claim 12, wherein the first morphology metricvalue is based on skewness of a first derivative of the first signal,and wherein the second morphology metric value is based on thepeak-to-peak modulation of the second signal.
 15. A system comprising:one or more inputs configured to: receive a first signal representativeof an intensity of light at a first detector of a regional oximetrysensor, and receive a second signal representative of an intensity oflight at a second detector of the regional oximetry sensor; and one ormore processors configured to: determine a first metric value based onthe first signal; determine a second metric value based on the secondsignal, determine a combination metric value based on the first metricvalue and the second metric value; determine, based on the combinationmetric value, that the regional oximetry sensor is not properlypositioned relative to a target site on a subject.
 16. The system ofclaim 15, wherein the one or more processors are configured to determinethat the regional oximetry sensor is not properly positioned relative tothe target site by at least comparing the combination metric value to adistribution threshold.
 17. The system of claim 15, wherein the one ormore processors are configured to use a neural network to determine,based on the combination metric value, that the regional oximetry sensoris not properly positioned relative to the target site.
 18. A methodcomprising: receiving a first signal representative of an intensity oflight at a first detector of a regional oximetry sensor, receiving asecond signal representative of an intensity of light at a seconddetector of the regional oximetry sensor; determining, by one or moreprocessors, a first metric value based on the first signal; determining,by the one or more processors, a second metric value based on the secondsignal; determining, by the one or more processors, a first combinationmetric value based on the first metric value and the second metricvalue; determining, by the one or more processors, a second combinationmetric value based on the first metric value and the second metricvalue; and determining, by one or more processors, based on the firstcombination metric value and the second combination metric value, thatthe regional oximetry sensor is properly positioned relative to a targetsite on a subject.
 19. The method of claim 18, wherein the determining,by the one or more processors, that the regional oximetry sensor isproperly positioned relative to the target site comprises comparing thefirst combination metric value to a first distribution threshold andcomparing the second combination metric value to a second distributionthreshold.
 20. The method of claim 19, wherein the determining, by theone or more processors, that the regional oximetry sensor is properlypositioned relative to the target site comprises determining that thefirst combination metric value exceeds the first distribution thresholdand that the second combination metric does not exceed the seconddistribution threshold.
 21. The method of claim 18, wherein thedetermining, by the one or more processors, that the regional oximetrysensor is properly positioned relative to the target site comprisesusing a neural network to determine that the regional oximetry sensor isproperly positioned relative to the target site based on the firstcombination metric value and the second combination metric value.
 22. Amethod comprising: receiving a first signal representative of anintensity of light at a first detector of a regional oximetry sensor;receiving a second signal representative of an intensity of light at asecond detector of the regional oximetry sensor; determining, by one ormore processors, a first metric value based on the first signal;determining, by the one or more processors, a second metric value basedon the second signal; determining, by the one or more processors, acombination metric value based on the first metric value and the secondmetric value; and determining, by one or more processors, based on thecombination metric value, that the regional oximetry sensor is notproperly positioned relative to a target site on a subject.
 23. Themethod of claim 22, wherein the determining, by the one or moreprocessors, that the regional oximetry sensor is not properly positionedrelative to the target site comprises comparing the combination metricvalue to a distribution threshold.
 24. The method of claim 22, whereinthe determining, by the one or more processors, that the regionaloximetry sensor is not properly positioned relative to the target sitecomprises using a neural network to determine that the regional oximetrysensor is properly positioned relative to the target based on thecombination metric value.